/*
 * LensKit, an open-source toolkit for recommender systems.
 * Copyright 2014-2017 LensKit contributors (see CONTRIBUTORS.md)
 * Copyright 2010-2014 Regents of the University of Minnesota
 *
 * Permission is hereby granted, free of charge, to any person obtaining
 * a copy of this software and associated documentation files (the
 * "Software"), to deal in the Software without restriction, including
 * without limitation the rights to use, copy, modify, merge, publish,
 * distribute, sublicense, and/or sell copies of the Software, and to
 * permit persons to whom the Software is furnished to do so, subject to
 * the following conditions:
 *
 * The above copyright notice and this permission notice shall be
 * included in all copies or substantial portions of the Software.
 *
 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
 * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
 * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
 * IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
 * CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
 * TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
 * SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
 */
package org.lenskit.bias;

import org.lenskit.data.dao.DataAccessObject;
import org.lenskit.data.ratings.Rating;
import org.lenskit.inject.Transient;
import org.lenskit.util.io.ObjectStream;

import javax.inject.Inject;
import javax.inject.Provider;

/**
 * Compute a bias model with the global average rating.
 */
public class GlobalAverageRatingBiasModelProvider implements Provider<GlobalBiasModel> {
    private final DataAccessObject dao;

    @Inject
    public GlobalAverageRatingBiasModelProvider(@Transient DataAccessObject dao) {
        this.dao = dao;
    }

    @Override
    public GlobalBiasModel get() {
        double sum = 0;
        int n = 0;
        try (ObjectStream<Rating> ratings = dao.query(Rating.class).stream()) {
            for (Rating r: ratings) {
                sum += r.getValue();
                n += 1;
            }
        }
        double mean = n > 0 ? sum / n : 0;
        return new GlobalBiasModel(mean);
    }
}
